LLMs can help explain credit risk models but not autonomously.
problem Leveraging LLMs for post-hoc explainability in credit risk models.
method Comparison of LLM outputs with SHAP and coefficient-based attributions on three LMs.
result LLMs reliably preserve feature-importance rankings but poorly align with autonomous explanations.
CrystalCandle creates user-friendly explanations for machine learning models.
problem Low trust in predictive models due to lack of interpretability.
method End-to-end pipeline for model interpretation, including Model Importer, Interpreter, Narrative Generator, and Exporter.
result CrystalCandle leads to higher adoption rates and improved downstream metrics.
Study finds strong link between crypto narratives and prices.
problem Understanding the impact of crypto narratives on prices.
method Topic modeling of Twitter data combined with sentiment analysis.
result Strong correlation between narratives and crypto prices.
Platform combines RL and language models to study narrative influence on AI decisions.
problem Understanding how narrative elements shape AI decision-making.
method Dual-system architecture with reinforcement learning and language model integration.
result Initial experiments show narrative frameworks can influence AI decision-making.
Automated prediction of valence, one key feature of a person's emotional state, from individuals' personal narratives may provide crucial information for mental healthcare (e.g. early diagnosis of mental diseases, supervision of disease course, etc.). In the Interspeech 2018 ComParE Self-Assessed Affect challenge, the …
Improved earnings predictions through text-morphed earnings calls.
problem Improving earnings prediction models using narrative information.
method Introducing a text-morphing methodology to generate counterfactual transcripts.
result Analysts over-react to sentiment and under-react to risk and uncertainty.
Algorithm improves SLR efficiency in financial narratives.
problem Fragmented understanding of financial narratives.
method NLP, clustering, interpretability tools.
result Unified narrative modeling approaches needed.
A new platform models how narratives influence financial markets.
problem Explaining irrational market behaviors through narratives.
method Integrated opinion dynamics and agent-based modeling.
result Initial results show how narratives shape financial outcomes.
Automatic understanding of domain specific texts in order to extract useful relationships for later use is a non-trivial task. One such relationship would be between railroad accidents' causes and their correspondent descriptions in reports. From 2001 to 2016 rail accidents in the U.S. cost more than $4.6B. Railroads i…
Paper analyzes systematic jump risk around the clock using news narratives.
problem Identifying and managing priced risks in real-time market conditions.
method Combining high-frequency market data with news narratives classified by an LLM.
result Significant heterogeneity in risk premia, with macroeconomic news commanding the largest premium.
Study finds whitepaper narratives do not predict market factor structure.
problem Predicting market behavior from cryptocurrency whitepaper claims.
method Zero-shot NLP classification combined with CP tensor decomposition of market data.
result Weak alignment between whitepaper claims and market statistics and latent factors.
Detects systematic anomalies in consumer complaints using NLP.
problem Detecting small, frequent anomalies in consumer complaints.
method NLP conversion of narratives, followed by anomaly detection algorithm.
result Demonstrates effectiveness of NLP for detecting systematic anomalies.
Study detects SLI in children from spontaneous narrative transcripts.
problem Detecting Specific Language Impairment (SLI) in children.
method Three-stage pipeline: feature extraction, dimensionality reduction, and classification.
result 97.13% accuracy in identifying SLI from transcripts.
Narrative disclosures in 10-K filings improve bankruptcy prediction beyond accounting ratios.
problem Traditional bankruptcy prediction models rely on accounting ratios, which may not capture early warning signals.
method Developed a PB Stress Score based on distress-specific language in 10-K narratives, evaluated against accounting and dictionary benchmarks.
result Adding the PB Stress Score increases AUC from 0.8323 to 0.9019 and improves top-decile bankruptcy capture from 44.12% to 64.71%.
Analyst reports contain valuable information for investment decisions.
problem Investment value in analyst reports is not fully understood or utilized.
method Embedded analyst reports with LLMs and ML forecasts of future returns.
result Portfolios formed on analyst report narratives outperform numerical forecasts and established factors.
Study shows GPT's earnings forecasts are human-like but not always accurate.
problem Information friction in AI-generated financial analysis.
method Examined GPT's earnings forecasts following corporate earnings releases and proposed a diagnostic framework.
result GPT's narrative attention is consistent and human-like but not always associated with higher forecast accuracy.
Attention mechanisms in deep neural networks have achieved excellent performance on sequence-prediction tasks. Here, we show that these recently-proposed attention-based mechanisms---in particular, the Transformer with its parallelizable self-attention layers, and the Memory Fusion Network with attention across modalit…
In this paper we present an early Apprenticeship Learning approach to mimic the behaviour of different players in a short adaption of the interactive fiction Anchorhead. Our motivation is the need to understand and simulate player behaviour to create systems to aid the design and personalisation of Interactive Narrativ…
FinCausal 2020 task detects financial document causality.
problem Detect causal relationships in financial documents.
method Binary classification and relation extraction tasks.
result 16 teams participated, 13 submitted system descriptions.
Developing an AI economist agent using RAG, knowledge graphs, and LLMs for economic scenario analysis.
problem Economic scenario analysis using large language models and knowledge graphs.
method Proposing an RAG-based AI economist framework that utilizes knowledge graphs and LLMs.
result Improves economic coherence and traceability in generated reports.
Analyzes news graphs to predict financial market dislocations.
problem Predicting financial market dislocations using news content.
method Extracts entities from news articles, aggregates them into graphs, applies network analysis, and uses sentiment analysis.
result Identifies high entropy in news graphs correlates with financial market dislocations.
Study shows diffused interface flows to single diffused balls over time.
problem Volume-preserving mean curvature flow in Euclidean space.
method Diffused interface version, exponential convergence proof.
result Exponential convergence to single diffused balls.
AR app visualizes Quranic Surah al-Fil for Islamic education.
problem Lack of interactive and context-rich learning materials for Quranic studies.
method Research and development approach, including data collection, user requirement analysis, interface design, 3D asset creation, and integration of AR technology.
result AR application achieved high accuracy and user satisfaction, enhancing learner engagement and understanding.
A text mining approach is proposed based on latent Dirichlet allocation (LDA) to analyze the Consumer Financial Protection Bureau (CFPB) consumer complaints. The proposed approach aims to extract latent topics in the CFPB complaint narratives, and explores their associated trends over time. The time trends will then be…
Paper develops a method for valid inference using language model predictions from verbal autopsy narratives.
problem Valid inference from verbal autopsy narratives for public health decision-making.
method Develops multiPPI++ method for valid inference using NLP techniques for COD prediction.
result Demonstrates the effectiveness of multiPPI++ in handling transportability issues and recovering ground truth estimates.
New interface explains contextual bandits to non-experts.
problem Interpreting and managing contextual bandits for non-expert operators.
method Developed a metric 'value gain' for off-policy evaluation and designed an interface to explain bandit behavior.
result Empowered non-experts to manage complex machine learning systems through accessible presentation.
This paper introduces the probabilistic module interface, which allows encapsulation of complex probabilistic models with latent variables alongside custom stochastic approximate inference machinery, and provides a platform-agnostic abstraction barrier separating the model internals from the host probabilistic inferenc…
We present a new recurrent neural network topology to enhance state-of-the-art machine learning systems by incorporating a broader context. Our approach overcomes recent limitations with extended narratives through a multi-layered computational approach to generate an abstract context representation. Therefore, the dev…
We consider the regularity of an interface between two incompressible and inviscid fluids flows in the presence of surface tension. We obtain local in time estimates on the interface in H23k+1 and the velocity fields in H23k. These estimates are obtained using geometric considerations which show th…
Deep model learns protein interfaces from high-order interactions.
problem Predicting protein interfaces from amino acid pairs.
method Graph neural networks and convolutional neural networks for 2D dense predictions.
result Our method consistently improves interface prediction performance.
Study boundary behavior of limit interfaces in Riemannian manifolds without convexity assumptions.
problem Boundary behavior of limit interfaces in Riemannian manifolds.
method Proves limit-interface is a free boundary varifold, integer rectifiable up to boundary.
result No convexity assumption required; valid even when limit-interface clusters near boundary.
Study on the regularity of p-Gauss curvature flow near flat interfaces.
problem Regularity of p-Gauss curvature flow near flat interfaces. method Analysis of convex hypersurface near the interface.
result Regularity of the convex hypersurface near the interface.
Analytic patch trees reveal new geometric structures and dimension fields.
problem Understanding the geometric and analytical properties of surface patch trees.
method Developed analytic surface patch trees and introduced interface curves to transmit state.
result Surface patch trees have natural foliations with one-dimensional curve trees and dimension fields.
Diffuse interface methods have recently been introduced for the task of semi-supervised learning. The underlying model is well-known in materials science but was extended to graphs using a Ginzburg--Landau functional and the graph Laplacian. We here generalize the previously proposed model by a non-smooth potential fun…
Common models for two-phase lipid bilayer membranes are based on an energy that consists of an elastic term for each lipid phase and a line energy at interfaces. Although such an energy controls only the length of interfaces, the membrane surface is usually assumed to be at least C1 across phase boundaries. We consi…
This paper uses bandit algorithms to reduce the cost of user interface experimentation in online retail.
problem Reducing the cost of user interface experimentation in online retail.
method Modeling user interface experimentation as an opportunistic bandit problem, reducing the cost of exploration.
result Significant regret reduction and improved contextual information for testing.
LLMs outperform human analysts in predicting earnings direction.
problem Evaluating financial statements without narrative or industry-specific information.
method Trained GPT4 on standardized, anonymous financial statements and instructed to predict earnings direction.
result LLMs predict earnings directionally with accuracy comparable to narrowly trained ML models.
We present sktime -- a new scikit-learn compatible Python library with a unified interface for machine learning with time series. Time series data gives rise to various distinct but closely related learning tasks, such as forecasting and time series classification, many of which can be solved by reducing them to relate…
User interfaces provide an interactive window between physical and virtual environments. A new concept in the field of human-computer interaction is a soft user interface; a compliant surface that facilitates touch interaction through deformation. Despite the potential of these interfaces, they currently lack a signal …
Study on evolving interfaces with complex curvature and density effects.
problem Understanding the dynamics of evolving heterogeneous elastic interfaces.
method Modeling an evolving curve with a density function, analyzing the associated gradient flow evolution.
result Analysis of the preservation and asymptotic behavior of geometric properties in the evolving system.
Study on liquid-vapor interfaces in stable equilibrium without assuming prior regularity.
problem Understanding the conditions for a liquid-vapor interface to be in stable equilibrium without assuming prior regularity.
method Proposes a weakest set of mathematical assumptions using varifold regularity theory and identifies a suitable stability condition.
result The liquid-vapor interface is a smoothly embedded analytic surface in stable equilibrium.
In this paper, we consider multi-valued graphs with a prescribed real analytic interface that minimize the Dirichlet energy. Such objects arise as a linearized model of area minimizing currents with real analytic boundaries and our main result is that their singular set is discrete in 2 dimensions. This confirms (and p…
We propose a deep learning strategy to estimate the mean curvature of two-dimensional implicit interfaces in the level-set method. Our approach is based on fitting feed-forward neural networks to synthetic data sets constructed from circular interfaces immersed in uniform grids of various resolutions. These multilayer …
New algorithm allows IGL to work with action-inclusive feedback.
problem IGL's failure in scenarios with action-inclusive feedback.
method Developed an algorithm and provided theoretical guarantees.
result Demonstrated effectiveness on large-scale experiments.
Objective: A variety of pattern analysis techniques for model training in brain interfaces exploit neural feature dimensionality reduction based on feature ranking and selection heuristics. In the light of broad evidence demonstrating the potential sub-optimality of ranking based feature selection by any criterion, we …
We propose that a simple, Lagrangian 2d N=(0,2) duality interface between the 3d N=2 XYZ model and 3d N=2 SQED can be associated to the simplest triangulated 4-manifold: the 4-simplex. We then begin to flesh out a dictionary between more general triangulated 4-manifolds with boundar…
Framework detects influential actors in disinformation networks.
problem Identifying and countering hostile influence operations on social media.
method Combines NLP, ML, graph analytics, and causal inference.
result 96% precision, 79% recall, 96% PR-curve area for IO detection.
Brain-Machine Interfaces (BMIs) have recently emerged as a clinically viable option to restore voluntary movements after paralysis. These devices are based on the ability to extract information about movement intent from neural signals recorded using multi-electrode arrays chronically implanted in the motor cortices of…